Battery management apparatus and method

ABSTRACT

Provided are battery management apparatuses and methods. The battery management apparatus includes a sensitivity determiner configured to determine sensitivity of a battery state based on sensed battery information and previous battery state information, and an execution parameter adjuster configured to adjust a parameter for estimating the battery state based on the determined sensitivity of the battery state.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/367,606 filed on Dec. 2, 2016, which claims the benefit under 35U.S.C. § 119(a) of Korean Patent Application No. 10-2015-0186642, filedon Dec. 24, 2015, in the Korean Intellectual Property Office, the entiredisclosures of which are incorporated herein by reference for allpurposes.

BACKGROUND 1. Field

The following description generally relates to an apparatus and methodfor battery management based on scheduling of a battery state estimationalgorithm.

2. Description of Related Art

Battery Management System (BMS) is used to optimize battery life. It isdesirous to have a BMS that accurately measures a battery state andcontrols modules related to temperature of a battery and charging anddischarging of a battery based on the measured state. In order toestimate a battery state, the BMS uses data such as, voltage,temperature, and current of a battery.

When data such as, voltage and current of a battery is measured orcalculated to estimate a battery state, data values, including themeasured or calculated voltage or current, may vary depending on batteryenvironments. Accordingly, measurement accuracy of battery data isvaluable for battery management.

In order to improve measurement accuracy of battery data or a batterystate, the algorithm complexity has increased, in which anelectrochemical model is used instead of a Coulomb counting method.Accuracy of an algorithm is important to increase efficiency in thebattery management system, but in this case, the unit of modules to becontrolled becomes smaller.

For this reason, the number of modules, of which SOC is to becalculated, is increased, which in turn increases the calculation of theentire algorithms of the BMS. Accordingly, as the requirements of theBMS are increased, complexity of system is increased, and powerconsumption is also increased.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

In one general aspect, there is provided a battery management apparatusincluding a processor configured to determine sensitivity of a batterystate based on sensed battery information and previous battery stateinformation, and adjust a parameter to estimate the battery state basedon the determined sensitivity of the battery state.

The processor may include a sensitivity determiner configured todetermine sensitivity of the battery state based on the sensed batteryinformation and the previous battery state information, and an executionparameter adjuster configured to adjust the parameter for estimating thebattery state based on the determined sensitivity of the battery state.

The sensitivity determiner may be configured to measure a variation perunit time in the battery state based on any one or any combination ofthe battery information and the previous battery state information, andto determine the sensitivity based on the measured variation per unittime.

The apparatus may include the battery information comprises any one orany combination of voltage, current, temperature, charge time, anddischarge time, and the previous battery state information may includeat least one of state of charge (SOC) or state of health (SOH).

The parameter may include any one or any combination of an executionperiod and an optimization process of a battery state estimationalgorithm.

The execution parameter adjuster may be further configured to shortenthe execution period of the battery state estimation algorithm, inresponse to an increase in the determined sensitivity.

The battery state estimation algorithm may include any one or anycombination of an SOC estimation algorithm and an SOH estimationalgorithm.

The processor may include an execution parameter determiner configuredto determine an execution parameter to be adjusted based on any one orany combination of a type of the battery state estimation algorithm anda calculated variation in the sensitivity.

The processor may include a scheduling period setter configured to setan initial operating period of each element of the battery managementapparatus as a sensing period of the battery information, and to adjustan operating period of the each element of the battery managementapparatus based on the previous battery state information.

The execution parameter determiner may be further configured todetermine the execution parameter to be adjusted and a value of theexecution parameter, in response to the calculated variation in thesensitivity exceeding a threshold.

The apparatus may include a memory configured to store instructions, andwherein the processor may be configured to execute the instructions todetermine sensitivity of a battery state based on sensed batteryinformation and previous battery state information, and to adjust aparameter to estimate the battery state based on the determinedsensitivity of the battery state.

In another general aspect, there is provided a battery management methodincluding determining sensitivity of a battery state based on sensedbattery information and previous battery state information, andadjusting an execution parameter to estimate the battery state based onthe determined sensitivity of the battery state.

The determining of the sensitivity may include measuring a variation perunit time in the battery state based on any one or any combination ofthe battery information and the previous battery state information, anddetermining the sensitivity based on the measured variation per unittime.

The battery information may include any one or any combination ofvoltage, current, temperature, charge time, and discharge time, and theprevious battery state information may include at least one of state ofcharge (SOC) and state of health (SOH).

The execution parameter may include any one or any combination of anexecution period and an optimization process of a battery stateestimation algorithm.

The adjusting of the execution parameter may include shortening theexecution period, in response an increase in the determined sensitivity.

The battery state estimation algorithm may include any one or anycombination of an SOC estimation algorithm and an SOH estimationalgorithm.

The method may include determining an execution parameter to be adjustedbased on any one or any combination of a type of the battery stateestimation algorithm and a calculated variation in the sensitivity.

The method may include setting an initial operating period of eachelement of a battery management apparatus as a sensing period of thebattery information, and adjusting an operating period of the eachelement of the battery management apparatus based on the previousbattery state information.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating an example of a battery managementapparatus.

FIG. 2 is a diagram illustrating an example of an SOC graph using anOpen Circuit Voltage (OCV) method that estimates battery life bymeasuring an open circuit voltage of a battery.

FIG. 3 is a diagram illustrating an example of a battery managementapparatus.

FIG. 4 is a diagram illustrating an example of a battery managementapparatus.

FIG. 5 is a diagram illustrating an example of a method of scheduling abattery state estimation algorithm.

FIG. 6 is a diagram illustrating an example of a method of scheduling abattery state estimation algorithm.

FIG. 7 is a diagram illustrating still an example of a batterymanagement apparatus.

FIG. 8 is a diagram illustrating an example of a battery managementapparatus.

Throughout the drawings and the detailed description, unless otherwisedescribed, the same drawing reference numerals will be understood torefer to the same elements, features, and structures. The relative sizeand depiction of these elements may be exaggerated for clarity,illustration, and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or apparatuses described herein. However, various changes,modifications, and equivalents of the methods, apparatuses, and/orapparatuses described herein will be apparent after an understanding ofthe disclosure of this application. For example, the sequences ofoperations described herein are merely examples, and are not limited tothose set forth herein, but may be changed as will be apparent after anunderstanding of the disclosure of this application, with the exceptionof operations necessarily occurring in a certain order. Also,descriptions of features that are known in the art may be omitted forincreased clarity and conciseness.

The features described herein may be embodied in different forms, andare not to be construed as being limited to the examples describedherein. Rather, the examples described herein have been provided merelyto illustrate some of the many possible ways of implementing themethods, apparatuses, and/or apparatuses described herein that will beapparent after an understanding of the disclosure of this application.

FIG. 1 is a diagram illustrating an example of a battery managementapparatus. In an example, the battery management apparatus 100 is anapparatus that manages a battery by dynamically scheduling a batterystate estimation algorithm (hereinafter referred to as an “algorithm”).In an example, the algorithm may include an algorithm that estimates aState Of Charge (SOC) or a State Of Health (SOH), and based on theestimation of a battery state, the algorithm may be classified as astatic algorithm that is initially scheduled or as a dynamic algorithmthat is scheduled dynamically, which will be described in detail below.The SOC refers to information on a quantity of electric charge of abattery, and the SOH refers to information on how much batteryperformance has deteriorated as compared to performance at the time ofmanufacture.

Referring to FIG. 1, the battery management apparatus 100 includes asensitivity determiner 110 and an execution parameter adjuster 120.

The sensitivity determiner 110 determines sensitivity of a battery statebased on at least one of sensed battery information and previous batterystate information. The battery information comprises information suchas, voltage, current, temperature, measuring time, and charge/dischargetime of the battery. The previous battery state information ispreviously measured information such as, for example, voltage, current,temperature, charge/discharge time, SOC and SOH information of thebattery.

In an example, the sensitivity determiner 110 determines sensitivity ofa battery state based on battery information or battery stateinformation that varies per unit time depending on the condition orambient environment of a battery. The battery condition includesconditions such as, for example, an internal temperature of a battery,manufacture characteristics, and the ambient environment may be anexternal temperature of a battery.

For example, when there is a small variation per unit time in thebattery information or battery state information, the sensitivitydeterminer 110 may determine sensitivity to be low, and when there is alarge variation per unit time in the battery information or batterystate information, the sensitivity determiner 110 may determinesensitivity to be high. A low sensitivity means the battery informationor battery state information, which is calculated using the batterystate estimation algorithm, is maintained for a long period of time,leading to a longer interval before executing a subsequent algorithm. Ahigh sensitivity means that the battery information or battery stateinformation is maintained for a short period of time, and may refer to acase where the battery information or battery state information per unittime changes rapidly. In this case, the battery management apparatus 100may adjust an algorithm execution period to be shorter to accuratelyestimate a battery state.

FIG. 2 is a diagram illustrating an example of an SOC graph using anOpen Circuit Voltage (OCV) method that estimates battery life bymeasuring an open circuit voltage of a battery, in which an OCVvariation dependent on the SOC are shown.

Referring to FIGS. 1 and 2, in section A the battery starts to dischargeafter being completely charged and in section C the battery is about tobe completely discharged. In Section C, there is a large variation inthe OCV dependent on a variation in the SOC, whereas in section B, thereis a small variation in the OCV. Section D is the transition betweenSections A and B, and Section E is the transition between Sections C andB. In Section D, the variation in the OCV decreases with the variationper unit time in the SOC, as compared to Section A. In Section E, thevariation in the OCV increases with the variation per unit time in theSOC, as compared to Section B. As the variation per unit time in the OCVvaries depending on whether the SOC of a battery is in section A, B, C,D, or E, the sensitivity determiner 110 determines differentsensitivities for each section.

For example, the sensitivity determiner 110 determines a highsensitivity for section A, since there is a large variation in the OCVdependent on the variation per unit time in the SOC in section A. Inanother example, the sensitivity determiner 110 determines a lowsensitivity for section B, since there is a small variation in the OCVdependent on the variation per unit time in the SOC in section B.

In an example, the execution parameter adjuster 120 adjusts an executionparameter value of an algorithm based on the determined sensitivity. Inan example, the execution parameter includes at least one of anexecution period or an optimization process of an algorithm. But, theexecution parameter is not limited thereto, and may include anyexecution parameter that may affect the accuracy and an execution timeof the algorithm.

The algorithm execution period is a parameter that determines accuracyof battery state estimation and an amount of power consumption. When theexecution period of an algorithm becomes shorter, estimation accuracy ofa battery state may be increased, but power consumption is alsoincreased. When the execution period of an algorithm becomes longer,estimation accuracy of a battery state is reduced, but power consumptionis also decreased.

The execution parameter adjuster 120 may adjust the algorithm executionperiod based on the determined sensitivity, in which the period may beadjusted in various manners according to criteria, such as, for example,a criterion that determines a priority between estimation accuracy of abattery state and/or the amount of power consumption. In an example, thecriteria may be predetermined.

For example, in the case where priority is put on the amount of powerconsumption, the execution parameter adjuster 120 may reduce the amountof power consumption of an algorithm execution module by adjusting analgorithm execution period to be longer, even when sensitivity isdetermined to be high. In an example where priority is put on theaccuracy of a battery state, rather than on the amount of powerconsumption, the execution parameter adjuster 120 may accuratelyestimate a battery state by adjusting an execution period of a dynamicalgorithm to be shorter, even when sensitivity is determined to be low.

In addition, among the execution parameters, the optimization process ofan algorithm may include determining which algorithm to apply and/orselecting a method of executing an algorithm. The method of executing analgorithm may include selecting either a dynamic algorithm or a staticalgorithm, or a combination of both. Other the execution parameters,such as, for example, remaining battery life, battery state, capacity,charging or discharging state of a battery, method of executing aselected algorithm, power consumption of an algorithm execution module,required time to execute an algorithm, and any other executionparameters that may affect an algorithm may be used for optimizationprocess of an algorithm without departing from the spirit and scope ofthe illustrative examples described.

The above-described optimization process of an algorithm is merelyillustrative, and execution parameter values of an algorithm may beadjusted by considering an ambient environment where a battery operates,charging and discharging state of a battery, temperature of a battery,and the number of battery modules.

In an example, the execution parameter adjuster 120 stores executionparameter values to be adjusted in a parameter storage (not shown) whichis designated for the execution of an algorithm.

FIG. 3 is a diagram illustrating another example of a battery managementapparatus. Referring to FIG. 3, the battery management apparatus 300includes a sensitivity determiner 310, an execution parameter determiner320, and an execution parameter adjuster 330. In addition to thedescription of FIG. 3 below, the above descriptions of FIGS. 1-2, arealso applicable to FIG. 3, and are incorporated herein by reference.Thus, the above description may not be repeated here.

The sensitivity determiner 310 determines sensitivity of a battery basedon at least one of sensed battery information and previous battery stateinformation.

In an example, the execution parameter determiner 320 determines anexecution parameter to be adjusted using the determined sensitivity, andbased on the types of an algorithm and/or a variation in sensitivity. Asdescribed above, the execution parameter includes at least one of anexecution period and an optimization process of an algorithm, and thevariation in sensitivity may be a difference in sensitivities determinedby the sensitivity determiner 310. In an example, the executionparameter determiner 320 calculates a difference between the sensitivitydetermined by the execution of a previous algorithm, and the sensitivitydetermined by the execution of a current algorithm, and may use thedifference as a variation in sensitivity.

For example, even when the SOC and SOH are estimated for a uniformperiod using an identical algorithm, the variation per unit time mayvary depending on a battery operating environment. The variation perunit time may increase when a battery operates at a high or lowtemperature, rather than at room temperature. Based on the sensedbattery information, the execution parameter determiner 320 maydetermine, according to a battery operating environment, an executionparameter to be adjusted, e.g., a temperature measuring period and anexecution period of a battery temperature management apparatus.

In another example, the variation per unit time in the SOH may also varydepending on a battery operating temperature. In the case where abattery is deteriorated below a specific threshold, the variation perunit time may be increased. Accordingly, the execution parameterdeterminer 320 may determine an execution parameter that checks whethera battery is deteriorated below a threshold, e.g., a battery voltagemeasurement period.

The execution parameter determiner 320 may calculate a variation betweena currently determined sensitivity and a previous sensitivity, and maydetermine an execution parameter to be adjusted and a value of theexecution parameter using the calculated variation. For example, whenthe calculated sensitivity variation exceeds a reference variation (θ),the execution parameter determiner 320 may determine an executionparameter, which most affects the accuracy, to be an execution parameterto be adjusted among various parameters. In an example, when asensitivity variation is not above the reference variation (θ), theremay be a slight change in an execution parameter, and even when theexecution parameter is adjusted, the execution parameter may not affectthe accuracy, such that the execution parameter determiner 320 maydetermine not to adjust the execution parameter to prevent unnecessaryresetting. The reference variation (θ) is a threshold to determine anexecution parameter, as compared to the sensitivity variation calculatedby the execution parameter determiner 320, and the reference variation(θ) may be initially set according to a purpose of a user or manager, ormay be updated by the battery management apparatus 300 according to anoperating environment and purpose of use of a battery.

Referring to FIG. 2, the variation in the OCV is increased with thevariation per unit time in the SOC in section A above, such thatsensitivity in section A may be determined to be high.

Accordingly, in the case where a current execution period is in sectionA even if the sensitivity determined for section A is high, theexecution parameter determiner 320 may determine a variation insensitivity, i.e., a difference between the previous sensitivity and thesensitivity determined in the current execution period, to be small.

Upon calculating the variation in sensitivity, the execution parameterdeterminer 320 may compare the sensitivity variation with the referencevariation (θ), and may determine a parameter to be adjusted based on thecomparison. For example, in response to the variation in sensitivitybeing below the reference variation (θ), the execution parameterdeterminer 320 may determine that a battery state is not suddenlychanged, and may omit the determination to adjust the parameter.

Section D is between section A, where the variation in the OCV isincreased with the variation per unit time in the OSC, and section B,where there is a small variation in the OCV dependent on the variationper unit time in the SOC. In section D, the execution parameterdeterminer 320 may determine that a sensitivity variation is large.

When there is a large variation in sensitivity, the execution parameterdeterminer 320 determines that a battery state is suddenly changed, andmay determine an algorithm execution parameter to be adjusted. Forexample, the execution parameter determiner 320 may determine to updatean algorithm execution period to measure a suddenly changing batterystate more accurately, and the execution parameter adjuster 330 mayadjust an execution period to be shorter.

FIG. 4 is a diagram illustrating an example of a battery managementapparatus. Referring to FIG. 4, the battery management apparatus 400includes a scheduling period setter 410, a sensitivity determiner 420,an execution parameter determiner 430, and an execution parameteradjuster 440. In addition to the description of FIG. 4 below, the abovedescriptions of FIGS. 1-3, are also applicable to FIG. 4, and areincorporated herein by reference. Thus, the above description may not berepeated here.

The scheduling period setter 410 sets an initial operating period ofeach element of the battery management apparatus 400 to be the same as abattery information sensing period, and may adjust the operating periodof each element of the battery management apparatus 400 based onprevious battery state information.

For example, the scheduling period setter 410 may performsynchronization of the initial operating period of the batterymanagement apparatus 400 with the battery information sensing period.Such setting of a scheduling period may be used to identify an initialbattery state. Based on the sensed battery information, the batterymanagement apparatus 400 may determine sensitivity, and repeat analgorithm updating cycle to identify a previous battery state, and mayapply an algorithm appropriate for a current battery state. For example,when the sensitivity determined based on the battery information andbattery state information is low, the scheduling period setter 410 mayset the battery information sensing period to be longer, and may performsynchronization of the operating period of the battery managementapparatus 400 with the determined battery information sensing period.

Further, the scheduling period setter 410 may set an execution period ofeach element by comparing power consumed for sensing the batteryinformation with power consumed by an algorithm execution module. Forexample, in the case where power consumed by an algorithm executionmodule is greater than power consumed for sensing battery information,the scheduling period setter 410 may perform sensing of batteryinformation twice when an algorithm is executed once, and may use anaverage of sensed battery information values.

FIG. 5 is a diagram illustrating an example of a method of scheduling abattery state estimation algorithm. The operations in FIG. 5 may beperformed in the sequence and manner as shown, although the order ofsome operations may be changed or some of the operations omitted withoutdeparting from the spirit and scope of the illustrative examplesdescribed. Many of the operations shown in FIG. 5 may be performed inparallel or concurrently. In addition to the description of FIG. 5below, the above descriptions of FIGS. 1-4, are also applicable to FIG.5, and are incorporated herein by reference. Thus, the above descriptionmay not be repeated here.

FIG. 5 may be an example performed by the battery management apparatus100 illustrated in FIG. 1. A general battery management apparatusinitially sets an execution parameter of a static algorithm, andexecutes the static algorithm by using the initially set executionparameter. As will be described with reference to FIG. 7, a scheduler710 may be mounted in a battery management apparatus 700, and may adjustin real time an execution parameter of a dynamic algorithm mounted in abattery management apparatus 700.

Referring to FIG. 5, in 510, the battery management apparatus 100 maydetermine sensitivity of a battery state based on at least one of sensedbattery information and battery state information. Variations per unittime in the battery information and previous battery state informationmay vary depending on the condition and ambient environment of abattery, and the battery management apparatus 100 may determinesensitivity based on such variations.

For example, sensitivity determined by the battery management apparatus100 is expressed in different units depending on the battery informationor battery state information used to determine sensitivity. A lowsensitivity means that a calculation result of the battery informationor battery state information is maintained for a long period of time,leading to a longer waiting time for executing a subsequent algorithm. Ahigh sensitivity means that a calculation result of the batteryinformation or battery state information is changes rapidly.

In the case of a high sensitivity, the battery management apparatus 100may adjust an algorithm execution period to be shorter to accuratelyestimate a battery state.

Referring to FIG. 2, variations in the OCV based on variations per unittime in the SOC may vary depending on whether the SOC of a battery is insection A, B, C, D, or E. Accordingly, sensitivity determined by thebattery management apparatus 100 may be different for each section.

For example, the battery management apparatus 100 may determine a highsensitivity for section A where there is a large variation in the OCVdependent on a variation per unit time in the SOC.

In another example, the battery management apparatus 100 determines anexecution parameter to be adjusted by comparing a sensitivity variationwith a reference variation (θ), and adjusts the determined executionparameter. The sensitivity variation refers to a sensitivity differencebetween a previous sensitivity and the sensitivity determined by thebattery management apparatus 100 upon executing a current algorithm.

In an example, the battery management apparatus 100 determines a highsensitivity for section A where there is a large variation in the OCVdependent on the variation in the SOC. In section A, a sensitivityvariation, i.e., a difference between the previous sensitivity and thesensitivity determined by the battery management apparatus 100 uponexecuting a current algorithm, may be calculated to be small.

In section D, which is between section A where sensitivity is determinedto be high and section B where sensitivity is determined to be low, asensitivity variation, i.e., a difference between the previoussensitivity and the sensitivity determined by the battery managementapparatus 100 upon executing a current algorithm, may be calculated tobe large. That is, a large variation in sensitivity indicates that abattery state is suddenly changed, and accordingly, the batterymanagement apparatus 100 may adjust an execution parameter to accuratelyidentify a suddenly changing battery state.

The battery management apparatus 100 may compare a reference variation(θ) with the sensitivity as determined. In an example, the referencevariation (θ) is predetermined.

For example, upon comparison of the reference variation (θ) with thesensitivity determined by the battery management apparatus 100, if avariation in the sensitivity is below the reference variation (θ), thebattery management apparatus 100 determines that the battery statecalculated by the algorithm is maintained for a long period of time, andmay determine not to update an execution parameter and an executionperiod. In this manner, power consumption of an algorithm executionmodule may be reduced, and the accuracy may be increased.

Referring back to FIG. 5, in 520, the battery management apparatus 100may adjust an execution parameter value of a dynamic algorithm based onsensitivity and/or sensitivity variations in sensed battery informationor battery state information.

The parameter of the battery management apparatus 100 may include anexecution period or an optimization process of an algorithm, but theexecution parameter is not limited thereto, and may include anyexecution parameter that may affect the accuracy and an execution timeof the algorithm.

For example, the battery management apparatus 300 determines anexecution parameter to be an execution period based on the calculatedsensitivity variation, and may adjust the execution period. For example,as shown in section D of FIG. 2, in the case where the sensitivityvariation is greater than the reference variation (θ), the batterymanagement apparatus 300 determines that the value calculated by thealgorithm is maintained for a long period of time, and may adjust anupdating period and an execution period of an algorithm to be longerthan a previous period.

FIG. 6 is a diagram illustrating an example of a method of scheduling abattery state estimation algorithm. The operations in FIG. 6 may beperformed in the sequence and manner as shown, although the order ofsome operations may be changed or some of the operations omitted withoutdeparting from the spirit and scope of the illustrative examplesdescribed. Many of the operations shown in FIG. 6 may be performed inparallel or concurrently. In addition to the description of FIG. 6below, the above descriptions of FIGS. 1-5, are also applicable to FIG.6, and are incorporated herein by reference. Thus, the above descriptionmay not be repeated here.

Referring to FIGS. 4 and 6, in 610, the battery management apparatus 400measures battery information. In addition to the battery informationmentioned above, in an example, the battery information includes basicinformation, such as, for example, power consumption of each elementincluded in the battery management apparatus 400, charge and/ordischarge time of a battery, which may be applied to update analgorithm.

In 620, the battery management apparatus 400 may determine sensitivityof a battery state based on the measured battery data. For example, thebattery management apparatus 400 determines sensitivity of a batterystate based on the battery information and/or battery state informationthat varies per unit time according to the condition or ambientenvironment of a battery.

In 630, the battery management apparatus 400 compares a sensitivityvariation, i.e., a difference between a previous sensitivity and thesensitivity determined by the battery management apparatus uponexecuting a current algorithm, with a reference variation (θ). In anexample, the reference variation (θ) may be initially set according to apurpose of a user or manager. In another example, the referencevariation (θ) may be updated by the battery management apparatus 300according to an operating environment and purpose of use of a battery.

For example, as described above with reference to FIG. 2, in section Awhere sensitivity is determined to be high, a sensitivity variation,i.e., a difference between the previous sensitivity and the sensitivitydetermined by the battery management apparatus 400 upon executing thealgorithm, may be determined to be small. In this case, the batterymanagement apparatus 400 may compare the calculated sensitivityvariation with the reference variation (θ). Upon comparison, if thesensitivity variation is below the reference variation (θ), the batterymanagement apparatus 400 may maintain a previously determined parameterwithout determining, updating, or adjusting an execution parameter of analgorithm.

Referring back to FIG. 2, in section D, which is between section A wheresensitivity is determined to be high, and section B where sensitivity isdetermined to be low, a sensitivity variation, i.e., a differencebetween the previous sensitivity and the sensitivity determined by thebattery management apparatus 400 upon executing a current algorithm, maybe calculated to be large. In this case, the battery managementapparatus 400 may compare the calculated sensitivity variation with thereference variation (θ). Upon comparison, if the sensitivity variationis greater than the reference variation (θ), in 640, the batterymanagement apparatus 400 may determine an algorithm execution parameterto be adjusted. Upon comparison, if the sensitivity variation is belowthe reference variation (θ), the battery management apparatus 400 mayperform sensing of the battery data again in 610.

In 650, upon determining the execution parameter to be adjusted, thebattery management apparatus 400 may adjust the execution parameter ofan algorithm. In an example, the execution parameter to be adjusted mayinclude an execution period or an optimization process of an algorithm,and may further include various execution parameters that affect themeasurement accuracy of a battery state. For example, by consideringsensitivity or power consumption of the battery management apparatus400, the battery information measurement period may be adjusted to befaster or slower. For example, by adjusting the measurement period ofvoltage, included in the battery information, to be faster, a voltagevariation of a battery may be measured more accurately.

As described above, by adjusting the measurement period of a batterystate, the voltage variation of a battery may be measured accurately,and reduction of the SOH to a level below a predetermined threshold maybe identified. In this case, by determining and adjusting an executionparameter of a battery state estimation algorithm, the battery life maybe estimated accurately.

The above methods are merely an example, and the present disclosure isnot limited thereto. Execution parameter values of an algorithm may beadjusted by considering factors such as, for example, ambientenvironment where a battery operates, charging and discharging state ofa battery, temperature of a battery, and number of battery modules.

FIG. 7 is a diagram illustrating still another example of a batterymanagement apparatus. Referring to FIG. 7, the battery managementapparatus 700 includes a scheduler 710, an algorithm controller 720, abattery manager 730, and a battery 740.

In an example, the scheduler 710 sets a specific value as an algorithmexecution parameter of the algorithm controller 720 at the time ofinitialization of an apparatus.

The scheduler 710 determines sensitivity of a battery state based onbattery information or battery state information that varies per unittime according to the condition or ambient environment of a battery. Inan example, in the case of a small variation per unit time in thebattery information or battery state information, the scheduler 710determines sensitivity to be low, and in the case of a large variationper unit time in the battery information or battery state information,the scheduler 710 determines sensitivity to be high.

A low sensitivity means that a result of calculation of the batteryinformation or battery state information, which is calculated by usingthe battery state estimation algorithm, is maintained for a long periodof time. A high sensitivity means that a result of calculation of thebattery information or battery state information, which is calculatedusing the battery state estimation algorithm, is maintained for a shortperiod of time, and may refer to a case where the battery information orbattery state information is suddenly changed. In this case, analgorithm execution period may be adjusted to be shorter to accuratelyestimate a battery state.

For example, referring to FIG. 2, the scheduler 710 may determinesensitivity using the variation in the OCV dependent on the variation inthe SOC. In the case where the variation in the OCV is increased withthe variation in the SOC remaining the same, sensitivity of a batterystate in that section of the SOC may be determined to be high. Upondetermining that sensitivity is high, the scheduler 710 may determinethat the SOC value per unit time calculated by using the batteryestimation algorithm is suddenly changed, and may adjust a period ofexecuting the battery estimation algorithm to be shorter for highestimation accuracy of a battery state.

The algorithm controller 720 may execute an algorithm according to analgorithm operation schedule determined based on sensitivity of abattery state, in which the algorithm controller 720 may include any oneof a static algorithm or a dynamic algorithm.

The static algorithm, which may be included in the algorithm controller720, may be executed regularly using an execution parameter set at theinitial state of a system, regardless of data newly input from anexternal source. Further, the algorithm controller 720 may include thedynamic algorithm in which an execution parameter may be changed in realtime. An execution period of the dynamic algorithm may be changed usingthe sensitivity determined by the scheduler 710, and an executionparameter determined based on a sensitivity variation, i.e., adifference between the previous sensitivity and the current sensitivity,and may be executed in different manners depending on the optimizationof the algorithm.

In an example, the static algorithm may be included or excluded and ifthe static algorithm is included, the number of static algorithm may bedetermined based on factors such as, for example, environment where thebattery management apparatus 700 is applied, a purpose of use of thebattery.

The battery manager 730 will be described below with reference to FIG.8.

FIG. 8 is a diagram illustrating yet another example of a batterymanagement apparatus.

Referring to FIG. 8, the battery management apparatus 800 includes ascheduler 810, an algorithm controller 820, a battery manager 830, abattery information sensor 840, a charge/discharge manager 850, and atemperature manager 860.

The battery manager 830 includes the battery information sensor 840, thecharging/discharging manager 850 that manages charging and dischargingof a battery based on an algorithm scheduled by the scheduler 810, andthe temperature manager 860 that manages temperature of a battery basedon measured battery information and a charging/discharging state of abattery.

In an example, the battery information sensor 840 includes a temperaturesensor 841, a voltage sensor 842, and a current sensor 843. Otherelements that measures basic information that may be applied to update abattery state estimation algorithm such as, for example, a power sensor(not shown) that measures power consumption of each element of thebattery management apparatus 800, a time sensor (not shown) thatmeasures charge or discharge time of a battery, may be used withoutdeparting from the spirit and scope of the illustrative examplesdescribed.

The battery information sensor 840 may transmit the measured batteryinformation to the scheduler 810, the charging/discharging manager 850,and the temperature manager 860.

The charging/discharging manager 850 may manage charging and dischargingof a battery based on a battery sensitivity determined by the scheduler810 and a sensitivity variation.

Referring to FIG. 2, the SOC of a battery decreases sharply in sectionC. If such sharp change in a battery state is not managed, overchargingor over-discharging of a battery may lead to damage or explosion of abattery. Accordingly, the charging/discharging manager 850 may blockpower supply to a battery in the case where there is a sharp change in abattery state.

For example, the charging/discharging manager 850 may estimate acharging state of a battery by using methods, such as, for example,Coulomb counting, equivalent circuit model, electrochemical model, ordata-based method. However, such methods are merely illustrative, suchthat charging and discharging of a battery may be managed using othermethods according to need by considering performance, and operationcharacteristics of a battery.

Based on the temperature, voltage, and current of a battery measured bythe battery information sensor 840, the temperature manager 860 maycontrol the internal temperature of a battery to be maintained within apredetermined range by controlling a cooling system or a heating system.

In an example, the battery management apparatus 100, battery managementapparatus 300, battery management apparatus 400, battery managementapparatus 700, and battery management apparatus 800 may be embedded inor interoperate with various digital devices such as, for example, amobile phone, a cellular phone, a smart phone, a wearable smart device(such as, for example, a ring, a watch, a pair of glasses, glasses-typedevice, a bracelet, an ankle bracket, a belt, a necklace, an earring, aheadband, a helmet, a device embedded in the cloths), a personalcomputer (PC), a laptop, a notebook, a subnotebook, a netbook, or anultra-mobile PC (UMPC), a tablet personal computer (tablet), a phablet,a mobile internet device (MID), a personal digital assistant (PDA), anenterprise digital assistant (EDA), a digital camera, a digital videocamera, a portable game console, an MP3 player, a portable/personalmultimedia player (PMP), a handheld e-book, an ultra mobile personalcomputer (UMPC), a portable lab-top PC, a global positioning system(GPS) navigation, a personal navigation device or portable navigationdevice (PND), a handheld game console, an e-book, and devices such as ahigh definition television (HDTV), an optical disc player, a DVD player,a Blue-ray player, a setup box, robot cleaners, a home appliance,content players, communication systems, image processing systems,graphics processing systems, other consumer electronics/informationtechnology (CE/IT) device, or any other device capable of wirelesscommunication or network communication consistent with that disclosedherein. The digital devices may be may be embedded in or interoperatewith a smart appliance, an intelligent vehicle, an electric vehicle, ahybrid vehicle, a smart home environment, or a smart buildingenvironment.

The battery management apparatus 100, battery management apparatus 300,battery management apparatus 400, battery management apparatus 700,battery management apparatus 800, sensitivity determiner 110, executionparameter adjuster 120, sensitivity determiner 310, execution parameterdeterminer 320, execution parameter adjuster 330, scheduling periodsetter 410, sensitivity determiner 420, execution parameter determiner430, execution parameter adjuster 440, scheduler 710, algorithmcontroller 720, battery manager 730, scheduler 810, algorithm controller820, battery manager 830, charge/discharge manager 850, and temperaturemanager 860 described in FIGS. 1, 3-4, and 7-8 that perform theoperations described in this application are implemented by hardwarecomponents configured to perform the operations described in thisapplication that are performed by the hardware components. Examples ofhardware components that may be used to perform the operations describedin this application where appropriate include controllers, sensors,generators, drivers, memories, comparators, arithmetic logic units,adders, subtractors, multipliers, dividers, integrators, and any otherelectronic components configured to perform the operations described inthis application. In other examples, one or more of the hardwarecomponents that perform the operations described in this application areimplemented by computing hardware, for example, by one or moreprocessors or computers. A processor or computer may be implemented byone or more processing elements, such as an array of logic gates, acontroller and an arithmetic logic unit, a digital signal processor, amicrocomputer, a programmable logic controller, a field-programmablegate array, a programmable logic array, a microprocessor, or any otherdevice or combination of devices that is configured to respond to andexecute instructions in a defined manner to achieve a desired result. Inone example, a processor or computer includes, or is connected to, oneor more memories storing instructions or software that are executed bythe processor or computer. Hardware components implemented by aprocessor or computer may execute instructions or software, such as anoperating system (OS) and one or more software applications that run onthe OS, to perform the operations described in this application. Thehardware components may also access, manipulate, process, create, andstore data in response to execution of the instructions or software. Forsimplicity, the singular term “processor” or “computer” may be used inthe description of the examples described in this application, but inother examples multiple processors or computers may be used, or aprocessor or computer may include multiple processing elements, ormultiple types of processing elements, or both. For example, a singlehardware component or two or more hardware components may be implementedby a single processor, or two or more processors, or a processor and acontroller. One or more hardware components may be implemented by one ormore processors, or a processor and a controller, and one or more otherhardware components may be implemented by one or more other processors,or another processor and another controller. One or more processors, ora processor and a controller, may implement a single hardware component,or two or more hardware components. A hardware component may have anyone or more of different processing configurations, examples of whichinclude a single processor, independent processors, parallel processors,single-instruction single-data (SISD) multiprocessing,single-instruction multiple-data (SIMD) multiprocessing,multiple-instruction single-data (MISD) multiprocessing, andmultiple-instruction multiple-data (MIMD) multiprocessing.

The methods illustrated in FIGS. 5-6 that perform the operationsdescribed in this application are performed by computing hardware, forexample, by one or more processors or computers, implemented asdescribed above executing instructions or software to perform theoperations described in this application that are performed by themethods. For example, a single operation or two or more operations maybe performed by a single processor, or two or more processors, or aprocessor and a controller. One or more operations may be performed byone or more processors, or a processor and a controller, and one or moreother operations may be performed by one or more other processors, oranother processor and another controller. One or more processors, or aprocessor and a controller, may perform a single operation, or two ormore operations.

The instructions or software to control computing hardware, for example,one or more processors or computers, to implement the hardwarecomponents and perform the methods as described above, and anyassociated data, data files, and data structures, may be recorded,stored, or fixed in or on one or more non-transitory computer-readablestorage media. Examples of a non-transitory computer-readable storagemedium include read-only memory (ROM), random-access memory (RAM), flashmemory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs,DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, magnetictapes, floppy disks, magneto-optical data storage devices, optical datastorage devices, hard disks, solid-state disks, and any other devicethat is configured to store the instructions or software and anyassociated data, data files, and data structures in a non-transitorymanner and provide the instructions or software and any associated data,data files, and data structures to one or more processors or computersso that the one or more processors or computers can execute theinstructions. In one example, the instructions or software and anyassociated data, data files, and data structures are distributed overnetwork-coupled computer systems so that the instructions and softwareand any associated data, data files, and data structures are stored,accessed, and executed in a distributed fashion by the one or moreprocessors or computers.

While this disclosure includes specific examples, it will be apparentafter an understanding of the disclosure of this application thatvarious changes in form and details may be made in these exampleswithout departing from the spirit and scope of the claims and theirequivalents. The examples described herein are to be considered in adescriptive sense only, and not for purposes of limitation. Descriptionsof features or aspects in each example are to be considered as beingapplicable to similar features or aspects in other examples. Suitableresults may be achieved if the described techniques are performed in adifferent order, and/or if components in a described system,architecture, device, or circuit are combined in a different manner,and/or replaced or supplemented by other components or theirequivalents. Therefore, the scope of the disclosure is defined not bythe detailed description, but by the claims and their equivalents, andall variations within the scope of the claims and their equivalents areto be construed as being included in the disclosure.

What is claimed is:
 1. A battery management apparatus comprising: aprocessor configured to: determine an execution parameter, of a batterystate estimation algorithm, to be adjusted, based on a type of thebattery state estimation algorithm used in estimating a battery state,using variation in sensed battery information and variation of previousbattery state information; determine not to adjust the executionparameter in response to either the variation in the sensed batteryinformation or the variation of the previous battery state informationbeing below a predetermined reference variation; and adjust thedetermined execution parameter to estimate, using the battery stateestimation algorithm having the adjusted execution parameter, thebattery state, wherein the execution parameter comprises any one or anycombination of an execution period, as an indication of when acorresponding battery estimation algorithm is to be executed, afrequency of the execution, and/or other execution timing control, andan optimization process of one or more optimization processes or otherprocesses of the corresponding battery state estimation algorithm. 2.The apparatus of claim 1, wherein the processor is further configured tomeasure the variation per unit time in the battery state based on anyone or any combination of the battery information and the previousbattery state information.
 3. The apparatus of claim 2, wherein theprocessor is further configured to shorten the execution period of thecorresponding battery state estimation algorithm, in response to anincrease in the measured variation per unit time.
 4. The apparatus ofclaim 1, wherein: the battery information comprises any one or anycombination of voltage, current, temperature, charge time, and dischargetime considered by the battery state estimation algorithm in theestimating of the battery state; and the previous battery stateinformation comprises at least one of state of charge (SOC) or state ofhealth (SOH).
 5. The apparatus of claim 1, wherein the correspondingbattery state estimation algorithm comprises any one or any combinationof an SOC estimation algorithm and an SOH estimation algorithm, asrespective types of battery state estimation algorithms.
 6. Theapparatus of claim 1, wherein the processor is further configured to setan initial operating period of each element of the battery managementapparatus as a sensing period of the battery information, and to adjustan operating period of the each element of the battery managementapparatus based on the previous battery state information.
 7. Theapparatus may of claim 1 comprises a memory configured to storeinstructions that, when executed by the processor, configure theprocessor to performing the determination of the execution parameter tobe adjusted, and the adjustment of the execution parameter to estimatethe battery state.
 8. A battery management method comprising:determining an execution parameter, of a battery state estimationalgorithm, to be adjusted, based on a type of the battery stateestimation algorithm used in estimating a battery state, using variationin sensed battery information and variation of previous battery stateinformation; determining not to adjust the execution parameter inresponse to either the variation in the sensed battery information orthe variation of the previous battery state information being below apredetermined reference variation; and adjusting the execution parameterto estimate, using the battery state estimation algorithm having theadjusted execution parameter, the battery state, wherein the executionparameter comprises any one or any combination of an execution period,as an indication of when a corresponding battery estimation algorithm isto be executed, a frequency of the execution, and/or other executiontiming control, and an optimization process of one or more optimizationprocesses or other processes of the corresponding battery stateestimation algorithm.
 9. The method of claim 8, further comprising:measuring the variation per unit time in the battery state based on anyone or any combination of the battery information and the previousbattery state information.
 10. The method of claim 9, wherein theadjusting of the execution parameter comprises shortening the executionperiod, in response to an increase in the measured variation per unittime.
 11. The method of claim 8, wherein: the battery informationcomprises any one or any combination of voltage, current, temperature,charge time, and discharge time considered by the battery stateestimation algorithm in the estimating of the battery state; and theprevious battery state information comprises at least one of state ofcharge (SOC) and state of health (SOH).
 12. The method of claim 8,wherein the battery state estimation algorithm comprises any one or anycombination of an SOC estimation algorithm and an SOH estimationalgorithm, as respective types of battery state estimation algorithms.13. The method of claim 8, further comprising: setting an initialoperating period of each element of a battery management apparatus as asensing period of the battery information; and adjusting an operatingperiod of the each element of the battery management apparatus based onthe previous battery state information.
 14. A non-transitorycomputer-readable storage medium storing instructions that, whenexecuted by a processor, cause the processor to perform the method ofclaim 8.